Download a fuzzy expert system design for diagnosis of

B.Tech, P.G.D.D.I.
DEPARTMENT OF BIOMEDICAL SIGNAL
PROCESSING & INSTRUMENTATION
SASTRA
TANJORE
•
Of all the Cancers in men, Prostate
cancer is one of the most common.
•
Prostate cancer is also one of the most
difficult cancers where early diagnosis is
very often difficult to make. Hence
decisions regarding management is still not
standardized.
•
We have made a FUZZY EXPERT SYSTEM
which gives to the user the patient
possibility ratio of the prostate cancer.
•
The patients who had symptoms of
prostatic disease were taken up for the
study
•
Patients were randomly selected and
were subjected to transabdominal ultrasound
(Using 3.5 MHZ Probe) for the study of
different zones. Images were obtained in
transverse and longitudinal planes
•
The prostate echo texture, and volume
were imaged
•
The four parameters (Prostate Volume,
Echo Texture, Total Acid Phosphatase,
Prostate fraction of the Acid Phosphatase)
were used as input and Prostate cancer
Risk (PCR) was determined using fuzzy
expert systems
•
If (ET is N) and (PAP is L) and (TAP
is L) and (PV is L) then (PCR is VL)
•
If (ET is N) and (PAP is VL) and
(TAP is VL) and (PV is VL) then (PCR is
N)
•
If (ET is N) and (PAP is VH) and
(TAP is VH) and (PV is VH) then (PCR is
VH)
TAP
(K.A.un
its)
PAP
(K.A.un
its)
PV (cc)
ET
Literat
ure (%)
FES (%)
2.47
2.24
1.77
1.59
1.24
1.54
1.45
1.26
1.14
1.04
87.3
80.7
58.9
48.7
34.8
D
I
N
N
N
75 –100
50 – 75
25 – 50
0 – 25
NIL
88.8
62.6
37.5
12.4
NIL
1. Ismail SARITAS, Novruz ALLAHVERDI and
Ibrahim Unal SERT, “A Fuzzy Expert System
Design for Diagnosis of Prostate Cancer”,
International Conference on Computer
Systems and Technologies, 2003.
2.
Lorenz A., Blum M., Ermert H., and Senge
Th., “Comparison of Different Neuro-Fuzzy
Classification Systems for the Detection
of Prostate Cancer in Ultrasonic Images”,
http://www.lp-it.de/neuro-fuzzyclassification.pdf
3. Zadeh, L. A. (1965). "Fuzzy sets,“
Information and Control, 8:338-353
4. http://www.mdsdx.com - Lab news, spring
issue, May 2001
5. Timothy J.Ross , “ Fuzzy logic with
Engineering Applications”, Mc Graw – Hill,
Inc., 1997.
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